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Nericell: Rich Monitoring of Road and Traffic Conditions
"... We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the develope ..."
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Cited by 173 (2 self)
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We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
Extracting a mobility model from real user traces
- In Proceedings of IEEE INFOCOM
, 2006
"... Abstract — Understanding user mobility is critical for simulations of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We p ..."
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Cited by 167 (1 self)
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Abstract — Understanding user mobility is critical for simulations of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17%. I.
SurroundSense: Mobile Phone Localization via Ambience
"... A growing number of mobile computing applications are centered around the user’s location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, ther ..."
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Cited by 156 (3 self)
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A growing number of mobile computing applications are centered around the user’s location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone’s camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87 % when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.
Self-Management in Chaotic Wireless Deployments
- In ACM MobiCom
, 2005
"... ABSTRACT Over the past few years, wireless networking technologies have made vast forays into our daily lives. Today, one can find 802.11 hardware and other personal wireless technology employed at homes, shopping malls, coffee shops and airports. Present-day wireless network deployments bear two im ..."
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Cited by 137 (9 self)
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ABSTRACT Over the past few years, wireless networking technologies have made vast forays into our daily lives. Today, one can find 802.11 hardware and other personal wireless technology employed at homes, shopping malls, coffee shops and airports. Present-day wireless network deployments bear two important properties: they are unplanned, with most access points (APs) deployed by users in a spontaneous manner, resulting in highly variable AP densities; and they are unmanaged, since manually configuring and managing a wireless network is very complicated. We refer to such wireless deployments as being chaotic.
Micro-Blog: Sharing and Querying Content Through Mobile Phones and Social Participation
- In Proc. ACM 6th Int’l Conf. on Mobile Systems, Applications, and Services (MOBISYS ’08
, 2008
"... Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be ..."
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Cited by 124 (13 self)
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Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
VTrack: Accurate, Energy-aware Road Traffic Delay Estimation Using Mobile Phones
"... Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of comm ..."
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Cited by 124 (7 self)
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Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors—GPS, WiFi, and/or cellular triangulation— opens up the attractive possibility of using position samples from drivers ’ phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don’t have GPS at
BreadCrumbs: Forecasting Mobile Connectivity
"... As mobile devices continue to shrink, users are no longer merely nomads, but truly mobile, employing devices on the move. At the same time, these users no longer rely on a single managed network, but exploit a wide variety of connectivity options as they spend their day. Together, these trends argue ..."
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Cited by 119 (3 self)
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As mobile devices continue to shrink, users are no longer merely nomads, but truly mobile, employing devices on the move. At the same time, these users no longer rely on a single managed network, but exploit a wide variety of connectivity options as they spend their day. Together, these trends argue that systems must consider the derivative of connectivity— the changes inherent in movement between separately managed networks, with widely varying capabilities. To manage the derivative of connectivity, we exploit the fact that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device’s owner, and customizes a predictive mobility model for that specific user. Rather than rely on a synthetic model or aggregate observations, this custom-tailored model can be used together with past observations of wireless network capabilities to generate connectivity forecasts. Applications can in turn use these forecasts to plan future network use with confidence. We have built a BreadCrumbs prototype, and evaluated it with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
Zee: Zero-effort Crowdsourcing for Indoor Localization
- In Proceedings of ACM International Conference on Mobile Computing and Networking (MobiCom
"... Radio Frequency (RF) fingerprinting, based on WiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at kno ..."
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Cited by 85 (2 self)
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Radio Frequency (RF) fingerprinting, based on WiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee – a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user’s initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length, (c) back propagation to go back and improve the accuracy of localization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.
Preserving location privacy in wireless LANs
- In Proceedings of 5th International Conference on Mobile Systems, Applications, and Services (MobiSys 2007
, 2007
"... The broadcast and tetherless nature of wireless networks and the widespread deployment of Wi-Fi hotspots makes it easy to remotely locate a user by observing her wireless signals. Location is private information and can be used by malicious individuals for blackmail, stalking, and other privacy viol ..."
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Cited by 67 (1 self)
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The broadcast and tetherless nature of wireless networks and the widespread deployment of Wi-Fi hotspots makes it easy to remotely locate a user by observing her wireless signals. Location is private information and can be used by malicious individuals for blackmail, stalking, and other privacy violations. In this paper, we analyze the problem of location privacy in wireless networks and present a protocol for improving location privacy. Our basic approach is to obfuscate several types of privacy-compromising information revealed by a mobile node, including sender identity, time of transmission, and signal strength. Our design is driven by realsystem implementation and field experiments along with analysis and simulations. Our system allows users to choose the level of privacy they desire, thereby increasing the performance of less private users (while not sacrificing private users ’ privacy at the same time). We evaluated our system based on real-life mobility data and wireless LAN coverage. Our results show that a user of our system can be indistinguishable from a thousand users in the same coverage area.
Automating cross-layer diagnosis of enterprise wireless networks
- In Proceedings of the ACM SIGCOMM Conference, Kyoto
, 2007
"... Modern enterprise networks are of sufficient complexity that even simple faults can be difficult to diagnose — let alone transient outages or service degradations. Nowhere is this problem more apparent than in the 802.11-based wireless access networks now ubiquitous in the enterprise. In addition to ..."
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Cited by 64 (8 self)
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Modern enterprise networks are of sufficient complexity that even simple faults can be difficult to diagnose — let alone transient outages or service degradations. Nowhere is this problem more apparent than in the 802.11-based wireless access networks now ubiquitous in the enterprise. In addition to the myriad complexities of the wired network, wireless networks face the additional challenges of shared spectrum, user mobility and authentication management. Not surprisingly, few organizations have the expertise, data or tools to decompose the underlying problems and interactions responsible for transient outages or performance degradations. In this paper, we present a set of analysis techniques and models to precisely determine all sources of data transfer delay due to media access and mobility in 802.11 networks — from the physical layer to the transport layer — as well as the interactions among them. While some sources of delay can be directly measured, many of the delay components, such as AP queuing, backoffs, contention, etc., must be inferred. To infer these delays from measurements, we develop a detailed model of MAC protocol behavior, both as it is described in the 802.11 specification as well as how it is implemented in vendor hardware. Combined with comprehensive traces of wireless activity taken from an enterprise network, we produce a complete delay breakdown for packet transmissions and pinpoint problems that constrain connectivity or limit performance. 1.
